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1

Sleep EEG analysis utilizing inter-channel covariance matrices

Gopan K. Gopika, Prabhu Sathvik S., Sinha Neelam

Biocybernetics and Biomedical Engineering

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2020

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Vol. 40, no. 1

527--545

EN

Background: Sleep is vital for normal body functions as sleep disorders can adversely affect a person. Electroencephalographic (EEG) signals indicate brain functions and have characteristic signatures for various sleep stages. These enable the use of EEG as an effective tool for in-depth studies about sleep. Sleep stages are broadly divided as rapid eye movement (REM) and non-rapid eye movement (NREM). NREM is further divided into 3 stages. The objective of the work is to distinguish the given EEG epoch as wake, NREM1, NREM2, NREM3 and REM. DREAMS Subject Database containing 5 EEG channels is used here. This work focuses on utilizing EEG by exploiting variations in inter-dependencies of different brain regions during sleep. New method: Covariance matrices of the wavelet-decomposed channels are used to obtain the variations in inter-dependencies. The feature sets are: (1) simple matrix properties(MF) like trace, determinant and norm, (2) eigen-values (E1), (3) eigen-vector corresponding to the largest eigen-value (E2) and (4) tangent vectors obtained using Riemann geometry (RG-TS). The features are input to ensemble classifier with bagging. Subject-specific, All-subjects-combined and Leave-one-subject-out methods of analysis are carried out. Results: In all methods of analysis, RG-TS features give maximum accuracy (80.05%, 83.05% and 61.79%), closely followed by E1 (79.49%, 77.14% and 58.34%). Comparison with existing method: The proposed method obtains higher and/or comparable accuracy. This work also ensures no biasing of classifier due to unequal class distribution. Conclusion: The performances of RG-TS and E1 features reveal that the changes in interdependencies of pre-frontal and occipital lobe along with the central lobe can be used to distinguish the different sleep stages.

2

A soft-computing based approach towards automatic detection of pulmonary nodule

Mukherjee Jhilam, Kar Madhuchanda, Chakrabarti Amlan, Das Sayan

Biocybernetics and Biomedical Engineering

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2020

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Vol. 40, no. 3

1036--1051

EN

Early detection of lung cancer is the major challenge for physicians to treat and control this deadly disease whose primary step is to detect pulmonary nodule from thoracic computed tomography (CT) images. In view of increasing the accuracy of the pulmonary nodule detection methodology, this paper proposes a novel technique that can aid early diagnosis of the patients. The study has considered high resolution computed tomography (HRCT) images from two public datasets LIDC and Lung-TIME and an independent dataset, created in collaboration between Peerless Hospital Kolkata and University of Calcutta. The key feature of the test dataset is that the class features are imbalanced in nature. The structures associated with lung parenchyma are segmented using parameterized multi-level thresholding technique, grayscale morphology, and rolling ball algorithm. Then random under sampling is implemented to overcome the imbalance class problem, followed by a feature selection methodology using binary particle swarm optimization (BPSO). The nodule and non-nodule classification are performed by implementing ensemble stacking. Indeed, it has been observed that there exists insufficient published literature that has been considered similar looking pulmonary abnormalities as non-nodule objects as well as imbalance class problem and feature selection algorithm to design an automated, accurate and robust model for automated detection of the pulmonary nodule. In reference to the LIDC dataset, the false positive, false negative detection rates and sensitivity are 1.01/scan, 0.56/scan and 99.01% respectively, which is an improvement in terms of accuracy as compared to the existing state-of-the-art research works.

3

Extracting mass concentration time series features for classifcation of indoor and outdoor atmospheric particulates

Hussain Lal, Aziz Wajid, Saeed Sharjil, Rafque Muhammad, Nadeem Malik Sajjad Ahmed, Shim Seong-O, Aftar Sania, Pirzada Jawad-ur-Rehman

Acta Geophysica

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2020

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Vol. 68, no. 3

945--963

EN

Particulate matters (PMs) are considered as one of the air pollutants generally associated with poor air quality in both outdoor and indoor environments. The composition, distribution and size of these particles hazardously afect the human health causing cardiovascular health problems, lung dysfunction, respiratory problems, chronic obstructive pulmonary disease and lungs cancer. Classifcation models developed by analyzing mass concentration time series data of atmospheric particulate matter can be used for the prediction of air quality and for issuing warnings to protect the health of the public. In this study, mass concentration time series data of both outdoor and indoor particulates matters PM2.5 (aerodynamics size up to 2.5 μ) and PM10.0 (aerodynamics size up to 10.0 μ) were acquired using Haz-Dust EPAM-5000 from six diferent locations of the Muzafarabad city, Azad Kashmir. The linear and nonlinear approaches were used to extract mass concentration time series features of the indoor and outdoor atmospheric particulates. These features were given as an input to the robust machine learning classifers. The support vector machine (SVM) kernels, ensemble classifers, decision tree and K-nearest neighbors (KNN) are used to classify the indoor and outdoor particulate matter time series. The performance was estimated in terms of area under the curve (AUC), accuracy, true negative rate, true positive rate, negative predictive value and positive predictive value. The highest accuracy (95.8%) was obtained using cubic and coarse Gaussian SVM along with the cosine and cubic KNN, while the highest AUC, i.e., 1.00, is obtained using fne Gaussian and cubic SVM as well as with the cubic and weighted KNN.

4

A classification framework for prediction of breast density using an ensemble of neural network classifiers

Kumar I., Bhadauria H. S., Virmani J., Thakur S.

Biocybernetics and Biomedical Engineering

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2017

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Vol. 37, no. 1

217--228

EN

The present work proposes a classification framework for the prediction of breast density using an ensemble of neural network classifiers. Expert radiologists, visualize the textural characteristics of center region of a breast to distinguish between different breast density classes. Accordingly, ROIs of fixed size are cropped from the center location of the breast tissue and GLCM mean features are computed for each ROI by varying interpixel distance 'd' from 1 to 15. The proposed classification framework consists of two stages, (a) first stage: this stage consists of a single 4-class neural network classifier NN0 (B-I/B-II/B-III/B-IV) which yields the output probability vector [PB-I PB-II PB-III PB-IV] indicating the probability values with which a test ROI belongs to a particular breast density class. (b) second stage: this stage consists of an ensemble of six binary neural network classifiers NN1 (B-I/B-II), NN2 (B-I/B-III), NN3 (B-I/B-IV), NN4 (B-II/B-III), NN5 (B-II/B-IV) and NN6 (B-III/B-IV). The output of the first stage of the classification framework, i.e. output on NN0 is used to obtain the two most probable classes for a test ROI. In the second stage this test ROI is passed through one of the binary neural networks, i.e. NN1 to NN6 corresponding to the two most probable classes predicted by NN0. [...]